Measuring channel planform change from image time series: A generalizable, spatially distributed, probabilistic method for quantifying uncertainty

Abstract. This paper presents a recent methodology developed for the analysis of the slow evolution of geophysical time series. The method is based on least-squares fitting of continuous line segments to the data, subject to flexible conditions, and is able to objectively locate the times of significant change in the series tendencies. The time distribution of these breakpoints may be an important set of parameters for the analysis of the long term evolution of some geophysical data, simplifying the intercomparison between datasets and offering a new way for the analysis of time varying spatially distributed data. Several application examples, using data that is important in the context of global warming studies, are presented and briefly discussed.

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Arctic Mackenzie Delta channel planform evolution during 1983-2013 utilising Landsat data and hydrological time series

Hydrological Processes ◽

10.1002/hyp.11315 ◽

2017 ◽

Vol 31 (22) ◽

pp. 3979-3995 ◽

Cited By ~ 4

Author(s):

Jenni-Mari Vesakoski ◽

Tua Nylén ◽

Berit Arheimer ◽

David Gustafsson ◽

Kristina Isberg ◽

...

Keyword(s):

Time Series ◽

Landsat Data ◽

Mackenzie Delta ◽

Channel Planform

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A Rational Probabilistic Method for Spatially Distributed Landslide Hazard Assessment

Environmental and Engineering Geoscience ◽

10.2113/10.1.27 ◽

2004 ◽

Vol 10 (1) ◽

pp. 27-43 ◽

Cited By ~ 51

Author(s):

W. C. HANEBERG

Keyword(s):

Hazard Assessment ◽

Probabilistic Method ◽

Landslide Hazard ◽

Landslide Hazard Assessment ◽

Spatially Distributed

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PAttern REtrieval or deNegation Testing Scheme (PARENTS) v.1.0 – Identifying the degree of presence of given patterns in spatial time series

10.5194/hess-2019-563 ◽

2019 ◽

Author(s):

Benjamin Müller ◽

Matthias Bernhardt ◽

Karsten Schulz

Keyword(s):

Time Series ◽

Principal Component ◽

Surface Characteristics ◽

Observation Time ◽

Soil Parameters ◽

Simple Test ◽

Testing Scheme ◽

Spatially Distributed ◽

Pattern Retrieval ◽

Test Algorithm

Abstract. The large number of spatially distributed earth observation products, i.e. time series of surface emissions and reflectances at different wavelengths with increasing spatial resolution, contribute to the derivation of surface characteristics, e.g. vegetation or soil parameters in the environmental sciences. These derivatives usually build upon complex algorithms consisting of atmospheric corrections and process descriptions. The testing scheme presented here seeks a different approach to identifying these surface characteristics that control the generation of such observation time series. Spatially distributed patterns of these characteristics of different persistence usually dominate parts of a time series because of their very specific reaction to and interaction with environmental influences. We test these characteristics' patterns for their existence in a rotated vector space of elementary patterns derived from a principal component analysis of an observational time series. With the result of this test we can then make valid assumptions, e.g. with regard to the importance of the surface properties for the emittance or reflectance, or their spatial uncertainties. We demonstrate the functionality of this rather simple test algorithm for a synthetic and fully traceable example, and an application in a medium hydrological catchment for a time series of thermal satellite data. Possible future applications for this scheme are the prioritization and improvement of model input, data assimilation, or the evaluation and validation of model output.

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Topographic effects on solar radiation distribution in mountainous watersheds and their influence on reference evapotranspiration estimates at watershed scale

Hydrology and Earth System Sciences ◽

10.5194/hess-14-2479-2010 ◽

2010 ◽

Vol 14 (12) ◽

pp. 2479-2494 ◽

Cited By ~ 65

Author(s):

C. Aguilar ◽

J. Herrero ◽

M. J. Polo

Keyword(s):

Time Series ◽

Solar Radiation ◽

High Speed ◽

Topographic Effects ◽

Require Time ◽

Radiation Fields ◽

Spatially Distributed ◽

Mountainous Watersheds ◽

Predicted Values ◽

Mean Differences

Abstract. Distributed energy and water balance models require time-series surfaces of the climatological variables involved in hydrological processes. Among them, solar radiation constitutes a key variable to the circulation of water in the atmosphere. Most of the hydrological GIS-based models apply simple interpolation techniques to data measured at few weather stations disregarding topographic effects. Here, a topographic solar radiation algorithm has been included for the generation of detailed time-series solar radiation surfaces using limited data and simple methods in a mountainous watershed in southern Spain. The results show the major role of topography in local values and differences between the topographic approximation and the direct interpolation to measured data (IDW) of up to +42% and −1800% in the estimated daily values. Also, the comparison of the predicted values with experimental data proves the usefulness of the algorithm for the estimation of spatially-distributed radiation values in a complex terrain, with a good fit for daily values (R2 = 0.93) and the best fits under cloudless skies at hourly time steps. Finally, evapotranspiration fields estimated through the ASCE-Penman-Monteith equation using both corrected and non-corrected radiation values address the hydrologic importance of using topographically-corrected solar radiation fields as inputs to the equation over uniform values with mean differences in the watershed of 61 mm/year and 142 mm/year of standard deviation. High speed computations in a 1300 km2 watershed in the south of Spain with up to a one-hour time scale in 30 × 30 m2 cells can be easily carried out on a desktop PC.

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Information content of stream level class data for hydrological model calibration

Hydrology and Earth System Sciences ◽

10.5194/hess-21-4895-2017 ◽

2017 ◽

Vol 21 (9) ◽

pp. 4895-4905 ◽

Cited By ~ 23

Author(s):

H. J. Ilja van Meerveld ◽

Marc J. P. Vis ◽

Jan Seibert

Keyword(s):

Time Series ◽

Citizen Science ◽

Model Calibration ◽

Hydrological Model ◽

Model Performance ◽

Added Value ◽

Distributed Data ◽

Vertical Resolution ◽

Level Data ◽

Spatially Distributed

Abstract. Citizen science can provide spatially distributed data over large areas, including hydrological data. Stream levels are easier to measure than streamflow and are likely also observed more easily by citizen scientists than streamflow. However, the challenge with crowd based stream level data is that observations are taken at irregular time intervals and with a limited vertical resolution. The latter is especially the case at sites where no staff gauge is available and relative stream levels are observed based on (in)visible features in the stream, such as rocks. In order to assess the potential value of crowd based stream level observations for model calibration, we pretended that stream level observations were available at a limited vertical resolution by transferring streamflow data to stream level classes. A bucket-type hydrological model was calibrated with these hypothetical stream level class data and subsequently evaluated on the observed streamflow records. Our results indicate that stream level data can result in good streamflow simulations, even with a reduced vertical resolution of the observations. Time series of only two stream level classes, e.g. above or below a rock in the stream, were already informative, especially when the class boundary was chosen towards the highest stream levels. There was some added value in using up to five stream level classes, but there was hardly any improvement in model performance when using more level classes. These results are encouraging for citizen science projects and provide a basis for designing observation systems that collect data that are as informative as possible for deriving model based streamflow time series for previously ungauged basins.

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Identification of Brain Functional Networks Using a Model-Based Approach

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001420570049 ◽

2019 ◽

Vol 34 (08) ◽

pp. 2057004

Author(s):

Vangelis P. Oikonomou ◽

Konstantinos Blekas ◽

Loukas Astrakas

Keyword(s):

Time Series ◽

Resting State ◽

Probabilistic Method ◽

Resting State Fmri ◽

Model Parameters ◽

Resting State Networks ◽

Model Based ◽

Functional Brain Networks ◽

Regression Mixture ◽

The Brain

Functional MRI (fMRI) is a valuable brain imaging technique. A significant problem, when analyzing fMRI time series, is the finding of functional brain networks when the brain is at rest, i.e. no external stimulus is applied to the subject. In this work, we present a probabilistic method to estimate the Resting State Networks (RSNs) using a model-based approach. More specifically, RSNs are assumed to be the result of a clustering procedure. In order to perform the clustering, a mixture of regression models are used. Furthermore, special care has been given in order to incorporate important functionalities, such as spatial and embedded sparsity enforcing properties, through the use of informative priors over the model parameters. Another interesting feature of the proposed scheme is the flexibility to handle all the brain time series at once, allowing more robust solutions. We provide comparative experimental results, using an artificial fMRI dataset and two real resting state fMRI datasets, that empirically illustrate the efficiency of the proposed regression mixture model.

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Data-Driven Mitigation of Energy Scheduling Inaccuracy in Renewable-Penetrated Grids: Summerside Electric Use Case

Energies ◽

10.3390/en12122228 ◽

2019 ◽

Vol 12 (12) ◽

pp. 2228

Author(s):

Mostafa Farrokhabadi

Keyword(s):

Time Series ◽

Energy Storage ◽

Energy Cost ◽

Prince Edward Island ◽

State Of The Art ◽

Negative Impact ◽

Probabilistic Method ◽

Data Driven ◽

Renewable Energy Resources ◽

The Impact

This paper presents findings on mitigating the negative impact of renewable energy resources variability on the energy scheduling problem, in particular for island grids and microgrids. The methods and findings presented in this paper are twofold. First, data obtained from the City of Summerside in the province of Prince Edward Island, Canada, is leveraged to demonstrate the effectiveness of state-of-the-art time series predictors in mitigating energy scheduling inaccuracy. Second, the outcome of the time series prediction analysis is used to propose a novel data-driven battery energy storage system (BESS) sizing study for energy scheduling purposes. The proposed probabilistic method accounts for intra-interval variations of generation and demand, thus mitigating the trade-off between time resolution of the problem formulation and the solution accuracy. In addition, as part of the sizing study, a BESS management strategy is proposed to minimize energy scheduling inaccuracies, and is then used to obtain the optimal BESS size. Finally, the paper presents quantitative analyses of the impact of both the energy predictors and the BESS on the supplied energy cost using the actual data of the Summerside Electric grid. The paper reveals the significant potential for reducing energy cost in renewable-penetrated grids and microgrids through state-of-the-art predictors combined with applications of properly-sized energy storage systems.

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Automatic subject-specific spatiotemporal feature selection for subject-independent affective BCI

PLoS ONE ◽

10.1371/journal.pone.0253383 ◽

2021 ◽

Vol 16 (8) ◽

pp. e0253383

Author(s):

Badar Almarri ◽

Sanguthevar Rajasekaran ◽

Chun-Hsi Huang

Keyword(s):

Time Series ◽

Feature Selection ◽

Emotion Recognition ◽

Learning Algorithm ◽

Feature Space ◽

Support Vector ◽

Affective Stimuli ◽

Subject Specific ◽

Spatially Distributed ◽

Valence And Arousal

The dimensionality of the spatially distributed channels and the temporal resolution of electroencephalogram (EEG) based brain-computer interfaces (BCI) undermine emotion recognition models. Thus, prior to modeling such data, as the final stage of the learning pipeline, adequate preprocessing, transforming, and extracting temporal (i.e., time-series signals) and spatial (i.e., electrode channels) features are essential phases to recognize underlying human emotions. Conventionally, inter-subject variations are dealt with by avoiding the sources of variation (e.g., outliers) or turning the problem into a subject-deponent. We address this issue by preserving and learning from individual particularities in response to affective stimuli. This paper investigates and proposes a subject-independent emotion recognition framework that mitigates the subject-to-subject variability in such systems. Using an unsupervised feature selection algorithm, we reduce the feature space that is extracted from time-series signals. For the spatial features, we propose a subject-specific unsupervised learning algorithm that learns from inter-channel co-activation online. We tested this framework on real EEG benchmarks, namely DEAP, MAHNOB-HCI, and DREAMER. We train and test the selection outcomes using nested cross-validation and a support vector machine (SVM). We compared our results with the state-of-the-art subject-independent algorithms. Our results show an enhanced performance by accurately classifying human affection (i.e., based on valence and arousal) by 16%–27% compared to other studies. This work not only outperforms other subject-independent studies reported in the literature but also proposes an online analysis solution to affection recognition.

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